ArticleScientific reports2026
Improving end-bearing capacity prediction of rock-socketed shafts using Gaussian-augmented optimized extreme gradient boosting models.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research employs conventional and optimized extreme gradient boosting (XGBoost) models to predict the end-bearing capacity ([Formula: see text]) of rock-socketed shafts. The arithmetic optimization (AOA), brainstorm optimization (BOA), and whale optimization (WOA) algorithms were used to optimize the XGBoost model. To conduct this research, a database of the [Formula: see text] of 151 rock-socketed shafts was compiled from the literature. The database (mentioned by O_Data) was preprocessed, and the [Formula: see text] of the 136 rock-socketed shafts was obtained. The Gaussian-noise technique was employed to create a synthetic database based on the [Formula: see text] of 136 rock-socketed shafts. A database of the [Formula: see text] of 500 rock-socketed shafts was generated and preprocessed. The [Formula: see text] of 460 rock-socketed shafts (136 original + 324 synthetic after preprocessing datasets) developed a second database (mentioned by OS_Data). The XGBoost, XGBoost_AOA, XGBoost_BOA, and XGBoost_WOA models were trained and tested using both databases. The performance analysis revealed that the XGBoost model estimated the [Formula: see text] with a root mean square error (RMSE) of 0.9205, mean absolute error of (MAE) of 0.7024, and a performance (R) of 0.9295 using the O_Data. Later, the performance of the XGBoost_AOA model was enhanced to 0.9894 using the OS_Data. It was also observed that OS_Data improved generalizability and reduced overfitting in the XGBoost_AOA model. Moreover, the multicollinearity analysis revealed that the rock mass rating (RMR) and geological strength index (GSI) exhibit problematic multicollinearity. In addition, the sensitivity analysis demonstrated that the RMR and GSI features have contributions of 20.301% and 20.369%, respectively, in estimating [Formula: see text]. For the first time, this research mapped a relationship between feature multicollinearity and sensitivity to analyze the overfitting of the soft computing models. Moreover, SHapley Additive exPlanations (SHAP) analysis identified compressive strength and rock mass rating as dominant predictors (0.65–1.36), while the geological strength index showed minimal influence (< 0.10). Finally, this research provides a Graphical User Interface application to help the geotechnical engineers and designers estimate the [Formula: see text].
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.